One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection
arXiv:2606.11202v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in dominant languages and has not progressed in parallel with multilingual capability, creating exploitable gaps for jailbreak attacks. Current jailbreak defenses are largely developed and evaluated in dominant languages, and their effectiveness is limited by the scarcity of aligned multilingual supervision and representations dispersion caused by language variation. To address this issue, we propose MLJai
The increasing global deployment of Large Language Models (LLMs) to multilingual users, coupled with safety training concentrated in dominant languages, creates immediate vulnerabilities that this research aims to address.
This research highlights a critical vulnerability in global AI deployments, where language-specific safety training can be circumvented, impacting the security and reliability of LLMs for diverse user bases and potentially enabling broader misuse.
The development of language-insensitive intention representations for multilingual jailbreak detection could significantly improve the robustness and safety of LLMs across different linguistic contexts, reducing exploitable gaps.
- · AI developers
- · Global LLM users
- · AI safety researchers
- · Multilingual communities
- · Malicious actors exploiting LLM vulnerabilities
Improved multilingual safety and robustness of LLMs.
Increased trust and adoption of AI technologies in non-dominant language markets.
Potential for new regulations or standards around multilingual AI safety and bias detection.
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Read at arXiv cs.CL